isprs annals IV 4 W2 77 2017
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
SPATIOTEMPORAL VISUALIZATION OF TIME-SERIES SATELLITE-DERIVED CO2
FLUX DATA USING VOLUME RENDERING AND GPU-BASED INTERPOLATION ON
A CLOUD-DRIVEN DIGITAL EARTH
Sensen Wu a, b, Yiming Yan a, b, Zhenhong Du a, b, *, Feng Zhang a, b, Renyi Liu a, b
a
Zhejiang Provincial Key Laboratory of Geographic Information System, Zhejiang University, Hangzhou 310028, China –
[email protected]; [email protected]
b
Institute of Geographic Information Science, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
KEY WORDS: Cloud-driven Digital Earth, Volume Rendering, GPU-based Interpolation, Time-series Satellite Data, Ocean
Carbon Cycle
ABSTRACT:
The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived
CO2 flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of
climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth
platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method
based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series
satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically
display the released or absorbed CO2 gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA
during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the
capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The
results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital
earth.
1. INTRODUCTION
Over the past two or three centuries, greenhouse gases produced
by human activities, e.g., carbon dioxide (CO2), have become
sufficiently widespread and been regarded as the main cause of
global warming (Canadell, 2003). The oceans, which play a
vital role in the global climatic change, can annually take in
approximately 30% of the anthropogenic CO2 from the
atmosphere (Feely, 2004; Millero, 1995). In addition, due to the
lack of efficient estimation techniques of the terrestrial carbon,
the land-sourced carbon is generally calculated through the
estimation results of ocean and atmosphere (Pan, 2014).
Therefore, an accurate comprehension of the distribution and
variability of ocean carbon is quite important for understanding
the basic structure of the global carbon cycle and providing
diagnostic insight into climate changes.
Satellite remote sensing, with its large-scale and real-time
advantages, has been widely used to evaluate the ocean carbon
flux and budget (Liu, 2014; Vasco, 2009). Time-series satellitederived CO2 (SateCO2) flux products are therefore produced
and applied to reveal the patterns of ocean carbon cycle.
Visualization techniques are regarded as a powerful manner to
analyze and present climate changes and environmental
simulations (Liu, 2014; Ziolkowska and Reyes, 2016). In
previous studies, the spatial distributions of ocean carbon were
usually demonstrated by thematic maps while the temporal
variations were displayed by diagrams (Benallal, 2017; Padhy,
2016; Parard, 2016; Schuster, 2013).
The ocean carbon changes through time and space
simultaneously. Traditional methods, however, can hardly
express its spatiotemporal dynamic process at multi-scales since
its characteristics of time and space are clarified separately.
Volume rendering technology, which explores the inner
structure of three-dimensional (3D) data and displays the
spatiotemporal dynamics of four-dimensional (4D) data by
adding time attribute, is an important branch of computer
graphics. It has been widely applied in various fields to
facilitate visual analytics and simulations. The SateCO2 flux
data that we focus on in this paper is essentially a time-space
dataset, and volume rendering techniques can be utilized for its
4D visualization to dynamically demonstrate the evolution of
CO2 gas as was done by Du et.al (2015).
However, the behaviors of the ocean carbon cycle are
controlled by various processes in global ocean environment
(Canadell, 2003). Although the method suggested by Du (2015)
has achieved visually realistic simulation of the ocean carbon
fluxes, their study mainly focused on regions. The role of these
regions in the global carbon cycle system has yet to be
discussed.
After the concept of ‘Digital Earth’ was presented by Gore in
1998, different commercial virtual globe platforms, e.g., Google
Earth and World Wind, were developed and have continued to
evolve over recent years (Gore, 1998; Liu, 2015). Virtual
globes provide a global coordinating framework to visualize
large-scale spatial datasets and to simulate geographic
phenomena. These globe platforms have been proved to be
* Corresponding author
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W2-77-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
useful for climate and marine researches (Brovelli, 2016;
Duarte, 2016; Li, 2011; Li, 2011; Sarthou, 2015; Shupeng and
van Genderen, 2008).
Considering the regional and global characteristics of the ocean
carbon cycle, utilizing volume rendering techniques to visualize
SateCO2 flux data on the virtual globe would be an improved
and much powerful solution for exploring the spatiotemporal
dynamics of ocean carbon sources and sinks. Due to the fact
that current commercial digital earth platforms do not directly
support volume visualization, attempts were carried out to
realize volume rendering by extending these platforms (Li,
2011; Liang, 2014; Liu, 2014; Liu, 2015) or developing their
own digital earth (Chen, 2012; Li, 2011; Sarthou, 2015).
However, studies on presenting the spatiotemporal variations of
ocean carbon on the globe using volume rendering are still rare
and needed further research. Also, volume rendering using
time-series SateCO2 flux data concerns GPU-based real-time
data processing, which requires great efforts to be integrated
into commercial virtual globes.
Therefore, a digital earth platform is developed in this article to
support the interactive analysis and display of multi-geospatial
data, and a framework of interactive volume rendering on the
virtual globe of 4D volume data generated from SateCO2 flux
data is designed and realized. In conclusion, we have four main
objectives in this paper: (1) to design a cloud-based data storage
pattern for managing multi-type data and providing distributed
computing capability for data analysis; (2) to develop a digital
earth platform to support multivariate analysis and visualization
in a cloud-based environment; (3) to dynamically create smooth
CO2 flux particles among space and time with a GPU-based
spatiotemporal interpolation approach; and (4) to realize
interactive volume rendering for the dynamic visualization of
the ocean carbon flux and budget on the virtual globe.
The remainder is organized as follows. Section 2 introduces the
spatiotemporal visualization framework of our research. In
Section 3, we provide a detailed implementation of our
approach, which includes a multi-source data store based on the
cloud, a digital earth to support multivariate visualization, a
technique to dynamically generate smooth 4D particles, and a
rendering method of volumetric data on the globe. Section 4
demonstrates the proposed approach using time-series SateCO2
flux data and discusses the visualization results over
conventional methods. Conclusions and future works are
presented in Section 5.
fragment code, such as compute unified device architecture
(CUDA) kernels and volume rendering shaders. Therefore,
GPU-based interactive volume rendering models using texture
slicing and particle system can be implemented on the renderer
engine. The detailed implementation of the framework is given
in Section 3.
PostgreSQL/PostGIS
Hadoop DFS
Distributed Computing
Multi-type Data
Basic geographic data
Observation data
Satellite data
Analysis Results
Data and Analysis Results Inputs
Digital Earth
Multi Sources
Earth
Engine
Viewer
Controller
PagedLOD Terrain
Renderer
Engine
(CPU/GPU)
Map Model
Analysis and Visualization
Spatiotemporal visualization of time-series SateCO2 flux data
Volume Rendering
CUDA
Mapping and
Interpolation
Slice 1
……
Slice 2
Slice N
Slicing
Textures
Blend
GPU
Figure 1. Spatiotemporal visualization framework on the digital
earth
3. IMPLEMENTATION
3.1 Cloud-based data management
In order to organize and manage multi-source data of various
types in our study, a hybrid data store is designed and
implemented by combining PPGIS with HDFS (Figure 2).
PostgreSQL, along with PostGIS, which provides spatial
objects for the PostgreSQL database, is employed to address the
structured data while HDFS, with its advantage of high
scalability and fault-tolerance, is applied to manage the volume
semi-structured or unstructured data.
PostgreSQL/PostGIS
2. SPATIOTEMPORAL VISUALIZATION
FRAMEWORK ON THE DIGITAL EARTH
Computing Engine
Hadoop DFS
Spatial Objects
Name
Node
Data
Node
Hadoop
Map Reduce
Attribute Tables
Data
Node
Data
Node
Spatial Spark
Data Service
Analysis Service
Result
As is shown in Figure 1, the framework of our spatiotemporal
visualization on the digital earth consists of three parts based on
the theory of modularized design. At the top is a data cloud that
manages multi-type data. In the middle layer is a digital earth,
where data analysis and visualization resides. The bottom layer
is a GPU-based interactive volume rendering model for
spatiotemporal visualization of time-series SateCO2 flux on the
virtual globe.
Specifically, the data cloud is implemented by combining
PostgreSQL-PostGIS (PPGIS) with Hadoop distributed file
system (HDFS), along with a distributed computing engine
developed to improve the data analysis efficiency. The digital
earth composes of an earth engine that constructs the
fundamental terrain globe, a viewer controller for processing
the handles of graphical user interface (GUI) and the earth
engine, and a renderer engine that supports various rendering
methods for spatiotemporal analysis and visualization. Notably,
the renderer engine is able to link and execute external GPU
Data Cloud
Web Services
Query
……
Data Service
Response
Request
Multi-type Data
Basic geographic data
Observation data
Analysis results
Satellite products
Satellite Imagery
Time Series Point
SST
SSW
Terrain Data (DEM)
Line Features
FLX
……
Time Series
Statistics
Band
Computation
Figure 2. Cloud-based data management
The diverse data in our research can be divided into three
categories: marine observation data, marine satellite-derived
data, and basic geographic data. Marine observation data
includes time-series fixed point attribute data, (e.g., real-time
ecological buoy recording nutrient parameters at a fixed
location) and line feature data representing measurement
attributes along a linear feature (e.g., ship observation data
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W2-77-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
along monitoring sections). Given that marine observation data
is mainly made up of records with spatial properties, these data
are handled in PPGIS. Marine satellite-derived data includes
various product types, such as sea surface wind (SSW), sea
surface temperature (SST), chlorophyll-a (CHL) and air-sea
CO2 flux (FLX). It is commonly organized in data grids and is
known as a classic type of unstructured data, and is therefore
distributedly stored in HDFS. Basic geographic data, as the
foundational spatial information to assist users to cognize
geographic locations and spatial correlations such as satellite
imagery, terrain data and place mark data, is structured in
custom image tile format and also managed by HDFS for
distributed searching and loading.
Moreover, a computing engine using Hadoop MapReduce and
Spatial Spark based on the data cloud is developed to enhance
the performance of complex spatiotemporal calculations, such
as long time-series statistics and complicated band computation
algorithm. Multiply data requests and analysis are done through
web services.
3.2 A digital earth to support multivariate visualization
Our digital earth is primarily developed in C++ and
OpenSceneGraph (OSG). The osgEarth library is integrated to
address geographic features while the GUI widgets are designed
by Qt. Libraries are adopted for specific usages, e.g., GDAL for
the read-write of spatial data. The structure of the digital earth
is shown in Figure 3.
Figure 3. Digital earth implementation architecture
The core module is an earth engine that organizes multiple data
and establishes the foundational terrain globe based on a paged
level of detail (LOD) map model. The viewer controller is in
charge of responding to the GUI and event handles, and
managing the earth engine accordingly, such as loading or
deleting data, updating display settings etc. The graphic shaders
for rendering the data map of the earth engine are addressed by
a renderer engine. Based on GLSL (OpenGL shading language)
and CUDA, custom shaders and GPU-parallel kernels can be
compiled with the renderer engine to realize customized volume
rendering for visualizing ocean carbon cycle. This will be
discussed detailedly in Section 3.3.
The MP terrain engine of osgEarth is used to construct the
terrain globe, and GDAL and feature_geom plugins are adopted
to display various spatial data, including the digital elevation
model (DEM), satellite imagery, satellite-derived products,
spatial vectors etc. These plugins are extended to support
multiple symbolic rendering of geospatial data, and the
visualization effects on the globe are shown in Figure 4.
Figure 4. Various spatial data are displayed on the virtual globe,
(a) satellite-derived SST product; (b) satellite-derived SSW
product; (c) in situ data of salinity; (d) satellite imagery and
DEM data
Using the distributed computing engine in the data cloud,
computing-intensive spatiotemporal analysis functions are also
provided in the digital earth. For example, the statistical results
of long time-series point analysis of daily CHL products from
2010 to 2015 can be obtained promptly within an acceptable
time (Figure 5).
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
then implemented to dynamically obtain spatiotemporal smooth
flux particles. The particles are mapped through parallel
coordinate transformation in GPU to be location-aware on the
digital earth. Rather than directly rendering polygon slices from
the 3D volume, a half-angle rendering technique proposed by
Green (2008) is adopted and extended in our research. Finally,
the slicing textures are composed and blended into the virtual
globe.
Figure 5. Time-series point statistics of the daily CHL products
from 2010 to 2015
To evaluate the capacity of the distributed computing engine,
performance tests of the time-series statistics were conducted.
The computing engine was deployed on a data cloud
implemented by Hadoop 2.7.0 and Spark 2.0.1 with 8 Dell
R720 servers. As we can see in Figure 6, compared to the
conventional one single server solution, our cloud solution
responded much more promptly with less execution time during
the increase of data amount. In addition, when the time length
of data reaches 16 years, the efficiencies of time-series point,
line, and polygon statistics have improved by almost 4, 5, and 7
times respectively, which further demonstrates the performance
of the computing engine.
Figure 6. Comparison of time-series statistics between cloud
solution and common solution
3.3 Location-aware interactive volume rendering of CO2
flux
By utilizing the renderer engine that we put forward in the
digital earth from last chapter, we proposed an interactive
volume rendering method implemented on the globe for the
time-series SateCO2 flux data (Figure 7).
3.3.1 Modeling of time-series SateCO2 flux data
Generally, the sea-water CO2 consists of riverine fluxes and airsea interface fluxes. In this paper, the SateCO2 flux data denotes
the air-sea CO2 fluxes. When the flux value is positive, CO2 is
emitted from water to air; otherwise, the water absorbs CO2.
The amount of stored or released carbon is quantified by carbon
budget, which can be calculated by integrating carbon flux
among a specific time and space. The positive or negative
budget value indicated a carbon source or a carbon sink.
According to the definition, the carbon budget (B) within an
area S over time T is:
T S
B
Flux( x, y, t )dsdt
(1)
0 0
where (x, y) is the position in S, and t is the time in T. To
render CO2 flux, a data model proposed by Du (2015) is
adopted to construct the air-sea SateCO2 flux data to a particle
system. The initial position of each particle is generated from a
random function and is located around its raster grid cell center.
The velocity of each particle is proportional to the CO2 flux
value. The motion direction (up or down) of particles is
determined by the flux value (positive or negative). Since the
cumulative gas volume is distinguished by particle velocity, the
carbon budgets can be represented by the depth or height of
particles. In addition, the variations of the fluxes are
demonstrated by color and size changes of particles. In our
study, a simple linear mapping transfer function is adopted to
convert the flux to particle color and size (Equation 2).
Particle (color , size) transfer ( Flux)
(2)
3.3.2 Spatiotemporal interpolation using CUDA
In this research, the current SateCO2 fluxes are usually monthly
average products, the time span of which would result in
temporal discontinuity during image presentation. Temporal
interpolation between time-adjacent images is hence required.
In the meantime, due to cloud covering or observational
constraints, some regions of satellite products are missing. To
achieve a better visualization result, spatial interpolation
techniques are applied to fill the missing parts. However, both
spatial and temporal interpolations require intensive
computation and substantial memory, which is unpractical for
large-scale geospatial data. Moreover, dynamic spatiotemporal
interpolation in real-time rendering is not yet supported by most
volume rendering algorithms. Therefore, to acquire a smooth
rendering result, we present an on-the-fly spatiotemporal
interpolation method using CUDA kernels.
Figure 7. Location-aware interactive volume rendering of CO2
flux
In order to render CO2 gas, we first design a data model to
generate particle systems from the CO2 flux products. A
spatiotemporal interpolation technique using CUDA kernels is
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W2-77-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
Figure 8. Spatiotemporal interpolation using CUDA
Figure 9. Cartesian coordinate system and geographic
coordinate system
In the rendering stage, the flux value of each frame is calculated
by its two time-adjacent satellite-derived products. As displayed
in Figure 8, the flux values of the same point in the two satellite
images are V1 and V2 at time T1 and T2, so the flux value Vk at
time Tk can be obtained using a linear mapping method
(Equation 3):
Due to the fact that GPU rendering pipeline does not support
spherical coordinates, the flux particles, which are originally
organized by GCS, require real-time transformation from GCS
to CCS in the rendering stage (Equation 4).
V V
Vk V1 2 1 Tk T1
T2 T1
(3)
To fill the missing region during real-time rendering, a GPUbased inverse distance weighting (IDW) algorithm is adopted in
our study using CUDA parallelism. IDW algorithm was first
proposed by Chaboissier (1968) and is widely used in
geosciences for spatial interpolation. It computes the predicted
value of an interpolated location via weighting average of
sample values.
Since shared memory is much faster than global memory in the
CUDA architecture, an optimization strategy that uses shared
memory is applied in our research to implement the IDW
interpolation (Mei, 2014). The detailed procedure is as follows:
(1) The coordinates of flux particles in global memory are
transported to shared memory.
(2) All threads from one block load the coordinates of each
particle and compute the distances and inverse weights to other
particles that are stored in current shared memory.
(3) The partial weights and weighted values of all particles are
utilized to calculate the prediction value of unknown particles.
(4) Predicted values are written back into global memory.
By making full use of the hardware’s capability for operating
both temporal and spatial mapping in CUDA, the particle
velocity can be recalculated at runtime when the view direction
changes.
3.3.3 Location-aware mapping on the virtual globe
Two types of coordinate systems are used in our digital earth
platform, i.e., geographic coordinate system (GCS) and
Cartesian coordinates system (CCS). A CCS represents
coordinates in 3D space through a tuple set (X, Y, Z) (Figure 9).
A GCS is a widely-used spherical coordinate system in
geosciences, which depicts spatial locations by latitude,
longitude, and altitude (Figure 9).
X R H cos lat sin lon
Y R H cos lat cos lon
Z R H sin lat
(4)
where (X,Y,Z) is the coordinates of CCS, R is the earth’s radius,
and H, lon, lat respectively denote the altitude (height),
longitude, and latitude of GCS. Since the transformations of
different particles are independent, parallel processing in
CUDA can be easily implemented to improve the efficiency.
3.3.4 Interactive volume rendering
Volume rendering using particle system is employed in this
research, which decouples rendering from simulation and
enables high quality rendering even from lower resolution grids
(Cohen, 2010). Rather than directly rendering polygon slices
from the 3D volume, a half-angle rendering technique proposed
by Green (2008) is adopted and extended to be applicable on
the digital earth. We first project the particles onto the virtual
globe and sort them along a half-angle axis, and render them in
slicing batches with shadow effects. The slicing textures are
then composed to form the final image, which is blended with
the virtual globe and is displayed on the screen in the end.
This technique obtains translucent and stereoscopic CO2 gas
with color and shadow effects on the globe, yet it still manages
to accelerate the rendering process significantly by using a
single 2D shadow buffer and avoiding repeated particle
information visiting.
In the implementation, the key idea is to compute the axis
which is half way from the light direction to view direction. The
rendering direction is determined by the relative orientation
between the viewer and the light.
Figure 10. Half-angle axis calculation based on the viewer and
light directions on the virtual globe
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
When the angle of the viewer and the light is less than 90°
(Figure 10a), the slices are rendered from front-to-back through
the viewer point; otherwise (Figure 10b), the slices are rendered
from back-to-front through the negative viewer direction.
The rendering process can be represented as follows:
(1) Generate or update the location and color of the flux
particles using spatiotemporal interpolation in CUDA.
(2) Map the coordinates of the particles to the digital earth
coordinate system.
(3) Calculate the half-angle vector and sort the particles along
the vector.
(4) Project the particles of each slice onto the slice plane and
render them from the viewpoint of both the viewer and the light.
(5) Compose slicing textures to form the final image and blend
it with the virtual globe.
(6) Repeat the above rendering steps to achieve a dynamic
simulation of the ocean carbon cycle.
4. RESULTS AND DISCUSSION
The framework proposed in Section 2 is implemented in C++.
OSG 3.2.0, osgEarth 2.7.0, and GDAL 1.10.1 library are used
for rendering and loading data. The spatiotemporal interpolation
of the flux particles and coordinate transformation are speeded
up by CUDA 8.0. The air-sea SateCO2 flux products organized
in HDF5 format are provided by the Second Institute of
Oceanography (SIO), State Oceanic Administration of China
(SOA). The monthly average products of the air-sea SateCO2
flux in 2009 covering the whole China Sea are chosen for the
simulation in this research. Detailed information of the data is
given in Table 1.
Table 1. Summary of the simulated air-sea SateCO2 flux data
Data Property
Value
Data Dimension
1800 × 2460 × 12
Latitude Extent
0~41° N
Longitude Extent
100~130° E
Time Extent
2009/01/01~2009/12/31
Spatial Resolution
0.0167°
Temporal Resolution
Monthly
Uncompressed Data Size
480 MB
4.1 Visualizations
Based on the spatiotemporal visualization framework, the
monthly average products of air-sea SateCO2 flux in 2009 are
utilized to generate the 4D volume particle data. The interactive
volume rendering of the air-sea CO2 flux and carbon budget on
the virtual globe is realized. In addition, both CPU and GPUbased spatiotemporal interpolations of flux particles are
implemented in the virtual globe environment, and the
performances of the two methods are compared as shown in
Figure 11. When the 4D smooth flux particles are created
within CPU, the average frame rate achieves at about 15 frames
per second (FPS) and varies violently during the rendering.
While, the average frame rate of the GPU-based
implementation acquires relatively higher FPS and maintains at
about 30 FPS benefited from the parallel generation of the flux
particles. It demonstrates that the spatiotemporal interpolation
strategy using GPU yields considerable improvements in the
rendering performance.
Figure 11. Performance comparisons of CPU and GPU-based
spatiotemporal interpolations in rendering
Figure 12 shows snapshots of different visualization results of
air-sea SateCO2 flux products. The original method projects and
renders satellite data on the globe by a fixed colorbar from blue
to red with the missing parts marked in black, as shown in
Figure 12a. Figure 12b, 12c and 12d show three new rendering
modes of flux particles, i.e., points, point sprites, and half-angle
volume rendering. The rendering of point sprites is
implemented by a geometry shader, and volume rendering is
performed based on the results of point sprites.
Figure 12. The original satellite data (a) and CO2 flux particles rendering with points (b), point sprites (c) and volume rendering (d)
on the globe
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https://doi.org/10.5194/isprs-annals-IV-4-W2-77-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
Figure 13 and Figure 14 show the dynamic simulation of air-sea
CO2 flux in the China Sea from August to December in 2009
using different rendering methods. In Figure 13, the evolution
of CO2 flux over time is shown by the color difference among
multiple images. Meanwhile, the spatial locations and
correlations are easily acquired from the GCS of the virtual
globe. Nevertheless, it can be seen that Figure 14 provides more
characteristics and presents information in greater details. In
addition to displaying the instant air-sea CO2 flux with color
change, the carbon budget accumulated since January is
represented by the altitude or depth of the particles. The
variations of ocean carbon sources or sinks are therefore easily
recognized from the continuous visualization results. For
example, the carbon source in the South China Sea (Region 1 in
Figure 14) releases more CO2 from August to September, and
the release drops down slowly on October and turns to absorb
CO2 on November. Region 2 of the Yellow Sea and the Bohai
Sea, however, shows as a large carbon sink consistently from
October to December. Besides, some novel phenomena are
observed from the results. For instance, although main areas of
the Bohai Sea act as carbon sinks in winter, its northern part
(Region 3 in Figure 14) presents as strong carbon sources and
releases vast amounts of CO2 with considerably high altitude
(carbon budget).
Figure 13. Air-sea CO2 flux dynamic simulation over time
represented by time-series satellite data.
Figure 14. Air-sea CO2 flux dynamic simulation over time represented by interactive volume rendering with real-time spatiotemporal
interpolation
4.2 Comparison and analysis
Compared to previous approaches, our method has several
advantages. First, time-series SateCO2 products are fully
utilized by transforming to 4D dataset, and projecting onto a
virtual globe so that the mechanisms of ocean carbon cycle are
better revealed. Second, the simulation is realized in a virtual
globe environment, thus, the locations and spatial relationships
are recognizable. In addition, numerous auxiliary geographic
data provided on the globe, such as DEM, satellite imagery,
place-markers etc., assist the user to better understand the
results. Furthermore, besides expressing the CO2 flux
information by color changes, the carbon budget is also
displayed in the altitude dimension.
Moreover, our dynamic temporal interpolation strategy
overcomes the restriction of not able to continuously express
the revolution over time (Figure 13), and achieves smooth
visualization effects on the virtual globe (Figure 14, T1 to T4).
Similarly, the missing regions of satellite data are parallelly
mapped in the GPU such that the entirely spatial characteristics
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of the ocean carbon cycle are visualized. For example, the
missing regions of the red frame in Figure 12a are filled
smoothly in Figure 12d.
5. CONCLUSIONS
The objective of our study is to propose a spatiotemporal
visualization framework for dynamic simulation of the ocean
carbon cycle using time-series SateCO2 flux data in a virtual
globe environment. First, a cloud-based data store is designed to
organize and manage multi-source data of various types. Then,
a digital earth platform is developed to support the interactive
analysis and display of multi-geospatial data on the cloud. After
that, an interactive volume rendering method implemented by
half-angle slicing and particle system is adopted to achieve
spatiotemporal visualization of air-sea CO2 flux on the globe.
The spatial distributions of CO2 flux are shown by color
changes while the variability and patterns of the carbon sinks
and sources are indicated in the altitude dimension. In addition,
a spatiotemporal interpolation method using CUDA during realtime rendering is designed to obtain smooth effects on both
spatial and temporal scales. Our experiments illustrate that the
suggested framework achieves considerably high FPS rates
(approximately 30 FPS) and visually realistic rendering effects.
Strategies that allow efficient volume rendering and dynamical
spatiotemporal interpolation are applicable and extensible to the
simulation of similar phenomena in geosciences.
Still, there are several issues that require consideration in future
works. Although our digital earth is developed on the cloud
computing architecture, the distributed computing ability is
only applied to data analysis. The effectiveness of rendering
methods using cloud computing capacity needs further study. In
addition, the ocean carbon cycle is a rather complicated process,
and its simulation and mechanism exploration need the
establishment of real marine environment (e.g., sea surface and
undersea environment) and the integration of more environment
parameters (e.g., SSW and ocean current).
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surface seawater of the Austral Ocean south of Australia.
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Du, Z., Fang, L., Bai, Y., Zhang, F. and Liu, R., 2015. Spatiotemporal visualization of air-sea CO2 flux and carbon budget
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Parard, G., Charantonis, A.A. and Rutgersson, A., 2016. Using
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This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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SPATIOTEMPORAL VISUALIZATION OF TIME-SERIES SATELLITE-DERIVED CO2
FLUX DATA USING VOLUME RENDERING AND GPU-BASED INTERPOLATION ON
A CLOUD-DRIVEN DIGITAL EARTH
Sensen Wu a, b, Yiming Yan a, b, Zhenhong Du a, b, *, Feng Zhang a, b, Renyi Liu a, b
a
Zhejiang Provincial Key Laboratory of Geographic Information System, Zhejiang University, Hangzhou 310028, China –
[email protected]; [email protected]
b
Institute of Geographic Information Science, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
KEY WORDS: Cloud-driven Digital Earth, Volume Rendering, GPU-based Interpolation, Time-series Satellite Data, Ocean
Carbon Cycle
ABSTRACT:
The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived
CO2 flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of
climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth
platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method
based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series
satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically
display the released or absorbed CO2 gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA
during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the
capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The
results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital
earth.
1. INTRODUCTION
Over the past two or three centuries, greenhouse gases produced
by human activities, e.g., carbon dioxide (CO2), have become
sufficiently widespread and been regarded as the main cause of
global warming (Canadell, 2003). The oceans, which play a
vital role in the global climatic change, can annually take in
approximately 30% of the anthropogenic CO2 from the
atmosphere (Feely, 2004; Millero, 1995). In addition, due to the
lack of efficient estimation techniques of the terrestrial carbon,
the land-sourced carbon is generally calculated through the
estimation results of ocean and atmosphere (Pan, 2014).
Therefore, an accurate comprehension of the distribution and
variability of ocean carbon is quite important for understanding
the basic structure of the global carbon cycle and providing
diagnostic insight into climate changes.
Satellite remote sensing, with its large-scale and real-time
advantages, has been widely used to evaluate the ocean carbon
flux and budget (Liu, 2014; Vasco, 2009). Time-series satellitederived CO2 (SateCO2) flux products are therefore produced
and applied to reveal the patterns of ocean carbon cycle.
Visualization techniques are regarded as a powerful manner to
analyze and present climate changes and environmental
simulations (Liu, 2014; Ziolkowska and Reyes, 2016). In
previous studies, the spatial distributions of ocean carbon were
usually demonstrated by thematic maps while the temporal
variations were displayed by diagrams (Benallal, 2017; Padhy,
2016; Parard, 2016; Schuster, 2013).
The ocean carbon changes through time and space
simultaneously. Traditional methods, however, can hardly
express its spatiotemporal dynamic process at multi-scales since
its characteristics of time and space are clarified separately.
Volume rendering technology, which explores the inner
structure of three-dimensional (3D) data and displays the
spatiotemporal dynamics of four-dimensional (4D) data by
adding time attribute, is an important branch of computer
graphics. It has been widely applied in various fields to
facilitate visual analytics and simulations. The SateCO2 flux
data that we focus on in this paper is essentially a time-space
dataset, and volume rendering techniques can be utilized for its
4D visualization to dynamically demonstrate the evolution of
CO2 gas as was done by Du et.al (2015).
However, the behaviors of the ocean carbon cycle are
controlled by various processes in global ocean environment
(Canadell, 2003). Although the method suggested by Du (2015)
has achieved visually realistic simulation of the ocean carbon
fluxes, their study mainly focused on regions. The role of these
regions in the global carbon cycle system has yet to be
discussed.
After the concept of ‘Digital Earth’ was presented by Gore in
1998, different commercial virtual globe platforms, e.g., Google
Earth and World Wind, were developed and have continued to
evolve over recent years (Gore, 1998; Liu, 2015). Virtual
globes provide a global coordinating framework to visualize
large-scale spatial datasets and to simulate geographic
phenomena. These globe platforms have been proved to be
* Corresponding author
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useful for climate and marine researches (Brovelli, 2016;
Duarte, 2016; Li, 2011; Li, 2011; Sarthou, 2015; Shupeng and
van Genderen, 2008).
Considering the regional and global characteristics of the ocean
carbon cycle, utilizing volume rendering techniques to visualize
SateCO2 flux data on the virtual globe would be an improved
and much powerful solution for exploring the spatiotemporal
dynamics of ocean carbon sources and sinks. Due to the fact
that current commercial digital earth platforms do not directly
support volume visualization, attempts were carried out to
realize volume rendering by extending these platforms (Li,
2011; Liang, 2014; Liu, 2014; Liu, 2015) or developing their
own digital earth (Chen, 2012; Li, 2011; Sarthou, 2015).
However, studies on presenting the spatiotemporal variations of
ocean carbon on the globe using volume rendering are still rare
and needed further research. Also, volume rendering using
time-series SateCO2 flux data concerns GPU-based real-time
data processing, which requires great efforts to be integrated
into commercial virtual globes.
Therefore, a digital earth platform is developed in this article to
support the interactive analysis and display of multi-geospatial
data, and a framework of interactive volume rendering on the
virtual globe of 4D volume data generated from SateCO2 flux
data is designed and realized. In conclusion, we have four main
objectives in this paper: (1) to design a cloud-based data storage
pattern for managing multi-type data and providing distributed
computing capability for data analysis; (2) to develop a digital
earth platform to support multivariate analysis and visualization
in a cloud-based environment; (3) to dynamically create smooth
CO2 flux particles among space and time with a GPU-based
spatiotemporal interpolation approach; and (4) to realize
interactive volume rendering for the dynamic visualization of
the ocean carbon flux and budget on the virtual globe.
The remainder is organized as follows. Section 2 introduces the
spatiotemporal visualization framework of our research. In
Section 3, we provide a detailed implementation of our
approach, which includes a multi-source data store based on the
cloud, a digital earth to support multivariate visualization, a
technique to dynamically generate smooth 4D particles, and a
rendering method of volumetric data on the globe. Section 4
demonstrates the proposed approach using time-series SateCO2
flux data and discusses the visualization results over
conventional methods. Conclusions and future works are
presented in Section 5.
fragment code, such as compute unified device architecture
(CUDA) kernels and volume rendering shaders. Therefore,
GPU-based interactive volume rendering models using texture
slicing and particle system can be implemented on the renderer
engine. The detailed implementation of the framework is given
in Section 3.
PostgreSQL/PostGIS
Hadoop DFS
Distributed Computing
Multi-type Data
Basic geographic data
Observation data
Satellite data
Analysis Results
Data and Analysis Results Inputs
Digital Earth
Multi Sources
Earth
Engine
Viewer
Controller
PagedLOD Terrain
Renderer
Engine
(CPU/GPU)
Map Model
Analysis and Visualization
Spatiotemporal visualization of time-series SateCO2 flux data
Volume Rendering
CUDA
Mapping and
Interpolation
Slice 1
……
Slice 2
Slice N
Slicing
Textures
Blend
GPU
Figure 1. Spatiotemporal visualization framework on the digital
earth
3. IMPLEMENTATION
3.1 Cloud-based data management
In order to organize and manage multi-source data of various
types in our study, a hybrid data store is designed and
implemented by combining PPGIS with HDFS (Figure 2).
PostgreSQL, along with PostGIS, which provides spatial
objects for the PostgreSQL database, is employed to address the
structured data while HDFS, with its advantage of high
scalability and fault-tolerance, is applied to manage the volume
semi-structured or unstructured data.
PostgreSQL/PostGIS
2. SPATIOTEMPORAL VISUALIZATION
FRAMEWORK ON THE DIGITAL EARTH
Computing Engine
Hadoop DFS
Spatial Objects
Name
Node
Data
Node
Hadoop
Map Reduce
Attribute Tables
Data
Node
Data
Node
Spatial Spark
Data Service
Analysis Service
Result
As is shown in Figure 1, the framework of our spatiotemporal
visualization on the digital earth consists of three parts based on
the theory of modularized design. At the top is a data cloud that
manages multi-type data. In the middle layer is a digital earth,
where data analysis and visualization resides. The bottom layer
is a GPU-based interactive volume rendering model for
spatiotemporal visualization of time-series SateCO2 flux on the
virtual globe.
Specifically, the data cloud is implemented by combining
PostgreSQL-PostGIS (PPGIS) with Hadoop distributed file
system (HDFS), along with a distributed computing engine
developed to improve the data analysis efficiency. The digital
earth composes of an earth engine that constructs the
fundamental terrain globe, a viewer controller for processing
the handles of graphical user interface (GUI) and the earth
engine, and a renderer engine that supports various rendering
methods for spatiotemporal analysis and visualization. Notably,
the renderer engine is able to link and execute external GPU
Data Cloud
Web Services
Query
……
Data Service
Response
Request
Multi-type Data
Basic geographic data
Observation data
Analysis results
Satellite products
Satellite Imagery
Time Series Point
SST
SSW
Terrain Data (DEM)
Line Features
FLX
……
Time Series
Statistics
Band
Computation
Figure 2. Cloud-based data management
The diverse data in our research can be divided into three
categories: marine observation data, marine satellite-derived
data, and basic geographic data. Marine observation data
includes time-series fixed point attribute data, (e.g., real-time
ecological buoy recording nutrient parameters at a fixed
location) and line feature data representing measurement
attributes along a linear feature (e.g., ship observation data
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along monitoring sections). Given that marine observation data
is mainly made up of records with spatial properties, these data
are handled in PPGIS. Marine satellite-derived data includes
various product types, such as sea surface wind (SSW), sea
surface temperature (SST), chlorophyll-a (CHL) and air-sea
CO2 flux (FLX). It is commonly organized in data grids and is
known as a classic type of unstructured data, and is therefore
distributedly stored in HDFS. Basic geographic data, as the
foundational spatial information to assist users to cognize
geographic locations and spatial correlations such as satellite
imagery, terrain data and place mark data, is structured in
custom image tile format and also managed by HDFS for
distributed searching and loading.
Moreover, a computing engine using Hadoop MapReduce and
Spatial Spark based on the data cloud is developed to enhance
the performance of complex spatiotemporal calculations, such
as long time-series statistics and complicated band computation
algorithm. Multiply data requests and analysis are done through
web services.
3.2 A digital earth to support multivariate visualization
Our digital earth is primarily developed in C++ and
OpenSceneGraph (OSG). The osgEarth library is integrated to
address geographic features while the GUI widgets are designed
by Qt. Libraries are adopted for specific usages, e.g., GDAL for
the read-write of spatial data. The structure of the digital earth
is shown in Figure 3.
Figure 3. Digital earth implementation architecture
The core module is an earth engine that organizes multiple data
and establishes the foundational terrain globe based on a paged
level of detail (LOD) map model. The viewer controller is in
charge of responding to the GUI and event handles, and
managing the earth engine accordingly, such as loading or
deleting data, updating display settings etc. The graphic shaders
for rendering the data map of the earth engine are addressed by
a renderer engine. Based on GLSL (OpenGL shading language)
and CUDA, custom shaders and GPU-parallel kernels can be
compiled with the renderer engine to realize customized volume
rendering for visualizing ocean carbon cycle. This will be
discussed detailedly in Section 3.3.
The MP terrain engine of osgEarth is used to construct the
terrain globe, and GDAL and feature_geom plugins are adopted
to display various spatial data, including the digital elevation
model (DEM), satellite imagery, satellite-derived products,
spatial vectors etc. These plugins are extended to support
multiple symbolic rendering of geospatial data, and the
visualization effects on the globe are shown in Figure 4.
Figure 4. Various spatial data are displayed on the virtual globe,
(a) satellite-derived SST product; (b) satellite-derived SSW
product; (c) in situ data of salinity; (d) satellite imagery and
DEM data
Using the distributed computing engine in the data cloud,
computing-intensive spatiotemporal analysis functions are also
provided in the digital earth. For example, the statistical results
of long time-series point analysis of daily CHL products from
2010 to 2015 can be obtained promptly within an acceptable
time (Figure 5).
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then implemented to dynamically obtain spatiotemporal smooth
flux particles. The particles are mapped through parallel
coordinate transformation in GPU to be location-aware on the
digital earth. Rather than directly rendering polygon slices from
the 3D volume, a half-angle rendering technique proposed by
Green (2008) is adopted and extended in our research. Finally,
the slicing textures are composed and blended into the virtual
globe.
Figure 5. Time-series point statistics of the daily CHL products
from 2010 to 2015
To evaluate the capacity of the distributed computing engine,
performance tests of the time-series statistics were conducted.
The computing engine was deployed on a data cloud
implemented by Hadoop 2.7.0 and Spark 2.0.1 with 8 Dell
R720 servers. As we can see in Figure 6, compared to the
conventional one single server solution, our cloud solution
responded much more promptly with less execution time during
the increase of data amount. In addition, when the time length
of data reaches 16 years, the efficiencies of time-series point,
line, and polygon statistics have improved by almost 4, 5, and 7
times respectively, which further demonstrates the performance
of the computing engine.
Figure 6. Comparison of time-series statistics between cloud
solution and common solution
3.3 Location-aware interactive volume rendering of CO2
flux
By utilizing the renderer engine that we put forward in the
digital earth from last chapter, we proposed an interactive
volume rendering method implemented on the globe for the
time-series SateCO2 flux data (Figure 7).
3.3.1 Modeling of time-series SateCO2 flux data
Generally, the sea-water CO2 consists of riverine fluxes and airsea interface fluxes. In this paper, the SateCO2 flux data denotes
the air-sea CO2 fluxes. When the flux value is positive, CO2 is
emitted from water to air; otherwise, the water absorbs CO2.
The amount of stored or released carbon is quantified by carbon
budget, which can be calculated by integrating carbon flux
among a specific time and space. The positive or negative
budget value indicated a carbon source or a carbon sink.
According to the definition, the carbon budget (B) within an
area S over time T is:
T S
B
Flux( x, y, t )dsdt
(1)
0 0
where (x, y) is the position in S, and t is the time in T. To
render CO2 flux, a data model proposed by Du (2015) is
adopted to construct the air-sea SateCO2 flux data to a particle
system. The initial position of each particle is generated from a
random function and is located around its raster grid cell center.
The velocity of each particle is proportional to the CO2 flux
value. The motion direction (up or down) of particles is
determined by the flux value (positive or negative). Since the
cumulative gas volume is distinguished by particle velocity, the
carbon budgets can be represented by the depth or height of
particles. In addition, the variations of the fluxes are
demonstrated by color and size changes of particles. In our
study, a simple linear mapping transfer function is adopted to
convert the flux to particle color and size (Equation 2).
Particle (color , size) transfer ( Flux)
(2)
3.3.2 Spatiotemporal interpolation using CUDA
In this research, the current SateCO2 fluxes are usually monthly
average products, the time span of which would result in
temporal discontinuity during image presentation. Temporal
interpolation between time-adjacent images is hence required.
In the meantime, due to cloud covering or observational
constraints, some regions of satellite products are missing. To
achieve a better visualization result, spatial interpolation
techniques are applied to fill the missing parts. However, both
spatial and temporal interpolations require intensive
computation and substantial memory, which is unpractical for
large-scale geospatial data. Moreover, dynamic spatiotemporal
interpolation in real-time rendering is not yet supported by most
volume rendering algorithms. Therefore, to acquire a smooth
rendering result, we present an on-the-fly spatiotemporal
interpolation method using CUDA kernels.
Figure 7. Location-aware interactive volume rendering of CO2
flux
In order to render CO2 gas, we first design a data model to
generate particle systems from the CO2 flux products. A
spatiotemporal interpolation technique using CUDA kernels is
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
Figure 8. Spatiotemporal interpolation using CUDA
Figure 9. Cartesian coordinate system and geographic
coordinate system
In the rendering stage, the flux value of each frame is calculated
by its two time-adjacent satellite-derived products. As displayed
in Figure 8, the flux values of the same point in the two satellite
images are V1 and V2 at time T1 and T2, so the flux value Vk at
time Tk can be obtained using a linear mapping method
(Equation 3):
Due to the fact that GPU rendering pipeline does not support
spherical coordinates, the flux particles, which are originally
organized by GCS, require real-time transformation from GCS
to CCS in the rendering stage (Equation 4).
V V
Vk V1 2 1 Tk T1
T2 T1
(3)
To fill the missing region during real-time rendering, a GPUbased inverse distance weighting (IDW) algorithm is adopted in
our study using CUDA parallelism. IDW algorithm was first
proposed by Chaboissier (1968) and is widely used in
geosciences for spatial interpolation. It computes the predicted
value of an interpolated location via weighting average of
sample values.
Since shared memory is much faster than global memory in the
CUDA architecture, an optimization strategy that uses shared
memory is applied in our research to implement the IDW
interpolation (Mei, 2014). The detailed procedure is as follows:
(1) The coordinates of flux particles in global memory are
transported to shared memory.
(2) All threads from one block load the coordinates of each
particle and compute the distances and inverse weights to other
particles that are stored in current shared memory.
(3) The partial weights and weighted values of all particles are
utilized to calculate the prediction value of unknown particles.
(4) Predicted values are written back into global memory.
By making full use of the hardware’s capability for operating
both temporal and spatial mapping in CUDA, the particle
velocity can be recalculated at runtime when the view direction
changes.
3.3.3 Location-aware mapping on the virtual globe
Two types of coordinate systems are used in our digital earth
platform, i.e., geographic coordinate system (GCS) and
Cartesian coordinates system (CCS). A CCS represents
coordinates in 3D space through a tuple set (X, Y, Z) (Figure 9).
A GCS is a widely-used spherical coordinate system in
geosciences, which depicts spatial locations by latitude,
longitude, and altitude (Figure 9).
X R H cos lat sin lon
Y R H cos lat cos lon
Z R H sin lat
(4)
where (X,Y,Z) is the coordinates of CCS, R is the earth’s radius,
and H, lon, lat respectively denote the altitude (height),
longitude, and latitude of GCS. Since the transformations of
different particles are independent, parallel processing in
CUDA can be easily implemented to improve the efficiency.
3.3.4 Interactive volume rendering
Volume rendering using particle system is employed in this
research, which decouples rendering from simulation and
enables high quality rendering even from lower resolution grids
(Cohen, 2010). Rather than directly rendering polygon slices
from the 3D volume, a half-angle rendering technique proposed
by Green (2008) is adopted and extended to be applicable on
the digital earth. We first project the particles onto the virtual
globe and sort them along a half-angle axis, and render them in
slicing batches with shadow effects. The slicing textures are
then composed to form the final image, which is blended with
the virtual globe and is displayed on the screen in the end.
This technique obtains translucent and stereoscopic CO2 gas
with color and shadow effects on the globe, yet it still manages
to accelerate the rendering process significantly by using a
single 2D shadow buffer and avoiding repeated particle
information visiting.
In the implementation, the key idea is to compute the axis
which is half way from the light direction to view direction. The
rendering direction is determined by the relative orientation
between the viewer and the light.
Figure 10. Half-angle axis calculation based on the viewer and
light directions on the virtual globe
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When the angle of the viewer and the light is less than 90°
(Figure 10a), the slices are rendered from front-to-back through
the viewer point; otherwise (Figure 10b), the slices are rendered
from back-to-front through the negative viewer direction.
The rendering process can be represented as follows:
(1) Generate or update the location and color of the flux
particles using spatiotemporal interpolation in CUDA.
(2) Map the coordinates of the particles to the digital earth
coordinate system.
(3) Calculate the half-angle vector and sort the particles along
the vector.
(4) Project the particles of each slice onto the slice plane and
render them from the viewpoint of both the viewer and the light.
(5) Compose slicing textures to form the final image and blend
it with the virtual globe.
(6) Repeat the above rendering steps to achieve a dynamic
simulation of the ocean carbon cycle.
4. RESULTS AND DISCUSSION
The framework proposed in Section 2 is implemented in C++.
OSG 3.2.0, osgEarth 2.7.0, and GDAL 1.10.1 library are used
for rendering and loading data. The spatiotemporal interpolation
of the flux particles and coordinate transformation are speeded
up by CUDA 8.0. The air-sea SateCO2 flux products organized
in HDF5 format are provided by the Second Institute of
Oceanography (SIO), State Oceanic Administration of China
(SOA). The monthly average products of the air-sea SateCO2
flux in 2009 covering the whole China Sea are chosen for the
simulation in this research. Detailed information of the data is
given in Table 1.
Table 1. Summary of the simulated air-sea SateCO2 flux data
Data Property
Value
Data Dimension
1800 × 2460 × 12
Latitude Extent
0~41° N
Longitude Extent
100~130° E
Time Extent
2009/01/01~2009/12/31
Spatial Resolution
0.0167°
Temporal Resolution
Monthly
Uncompressed Data Size
480 MB
4.1 Visualizations
Based on the spatiotemporal visualization framework, the
monthly average products of air-sea SateCO2 flux in 2009 are
utilized to generate the 4D volume particle data. The interactive
volume rendering of the air-sea CO2 flux and carbon budget on
the virtual globe is realized. In addition, both CPU and GPUbased spatiotemporal interpolations of flux particles are
implemented in the virtual globe environment, and the
performances of the two methods are compared as shown in
Figure 11. When the 4D smooth flux particles are created
within CPU, the average frame rate achieves at about 15 frames
per second (FPS) and varies violently during the rendering.
While, the average frame rate of the GPU-based
implementation acquires relatively higher FPS and maintains at
about 30 FPS benefited from the parallel generation of the flux
particles. It demonstrates that the spatiotemporal interpolation
strategy using GPU yields considerable improvements in the
rendering performance.
Figure 11. Performance comparisons of CPU and GPU-based
spatiotemporal interpolations in rendering
Figure 12 shows snapshots of different visualization results of
air-sea SateCO2 flux products. The original method projects and
renders satellite data on the globe by a fixed colorbar from blue
to red with the missing parts marked in black, as shown in
Figure 12a. Figure 12b, 12c and 12d show three new rendering
modes of flux particles, i.e., points, point sprites, and half-angle
volume rendering. The rendering of point sprites is
implemented by a geometry shader, and volume rendering is
performed based on the results of point sprites.
Figure 12. The original satellite data (a) and CO2 flux particles rendering with points (b), point sprites (c) and volume rendering (d)
on the globe
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-4-W2-77-2017 | © Authors 2017. CC BY 4.0 License.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
Figure 13 and Figure 14 show the dynamic simulation of air-sea
CO2 flux in the China Sea from August to December in 2009
using different rendering methods. In Figure 13, the evolution
of CO2 flux over time is shown by the color difference among
multiple images. Meanwhile, the spatial locations and
correlations are easily acquired from the GCS of the virtual
globe. Nevertheless, it can be seen that Figure 14 provides more
characteristics and presents information in greater details. In
addition to displaying the instant air-sea CO2 flux with color
change, the carbon budget accumulated since January is
represented by the altitude or depth of the particles. The
variations of ocean carbon sources or sinks are therefore easily
recognized from the continuous visualization results. For
example, the carbon source in the South China Sea (Region 1 in
Figure 14) releases more CO2 from August to September, and
the release drops down slowly on October and turns to absorb
CO2 on November. Region 2 of the Yellow Sea and the Bohai
Sea, however, shows as a large carbon sink consistently from
October to December. Besides, some novel phenomena are
observed from the results. For instance, although main areas of
the Bohai Sea act as carbon sinks in winter, its northern part
(Region 3 in Figure 14) presents as strong carbon sources and
releases vast amounts of CO2 with considerably high altitude
(carbon budget).
Figure 13. Air-sea CO2 flux dynamic simulation over time
represented by time-series satellite data.
Figure 14. Air-sea CO2 flux dynamic simulation over time represented by interactive volume rendering with real-time spatiotemporal
interpolation
4.2 Comparison and analysis
Compared to previous approaches, our method has several
advantages. First, time-series SateCO2 products are fully
utilized by transforming to 4D dataset, and projecting onto a
virtual globe so that the mechanisms of ocean carbon cycle are
better revealed. Second, the simulation is realized in a virtual
globe environment, thus, the locations and spatial relationships
are recognizable. In addition, numerous auxiliary geographic
data provided on the globe, such as DEM, satellite imagery,
place-markers etc., assist the user to better understand the
results. Furthermore, besides expressing the CO2 flux
information by color changes, the carbon budget is also
displayed in the altitude dimension.
Moreover, our dynamic temporal interpolation strategy
overcomes the restriction of not able to continuously express
the revolution over time (Figure 13), and achieves smooth
visualization effects on the virtual globe (Figure 14, T1 to T4).
Similarly, the missing regions of satellite data are parallelly
mapped in the GPU such that the entirely spatial characteristics
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4/W2, 2017
2nd International Symposium on Spatiotemporal Computing 2017, 7–9 August, Cambridge, USA
of the ocean carbon cycle are visualized. For example, the
missing regions of the red frame in Figure 12a are filled
smoothly in Figure 12d.
5. CONCLUSIONS
The objective of our study is to propose a spatiotemporal
visualization framework for dynamic simulation of the ocean
carbon cycle using time-series SateCO2 flux data in a virtual
globe environment. First, a cloud-based data store is designed to
organize and manage multi-source data of various types. Then,
a digital earth platform is developed to support the interactive
analysis and display of multi-geospatial data on the cloud. After
that, an interactive volume rendering method implemented by
half-angle slicing and particle system is adopted to achieve
spatiotemporal visualization of air-sea CO2 flux on the globe.
The spatial distributions of CO2 flux are shown by color
changes while the variability and patterns of the carbon sinks
and sources are indicated in the altitude dimension. In addition,
a spatiotemporal interpolation method using CUDA during realtime rendering is designed to obtain smooth effects on both
spatial and temporal scales. Our experiments illustrate that the
suggested framework achieves considerably high FPS rates
(approximately 30 FPS) and visually realistic rendering effects.
Strategies that allow efficient volume rendering and dynamical
spatiotemporal interpolation are applicable and extensible to the
simulation of similar phenomena in geosciences.
Still, there are several issues that require consideration in future
works. Although our digital earth is developed on the cloud
computing architecture, the distributed computing ability is
only applied to data analysis. The effectiveness of rendering
methods using cloud computing capacity needs further study. In
addition, the ocean carbon cycle is a rather complicated process,
and its simulation and mechanism exploration need the
establishment of real marine environment (e.g., sea surface and
undersea environment) and the integration of more environment
parameters (e.g., SSW and ocean current).
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